192 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" positions in Norway
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independently and in a team. Drive to learn new methods and applications. Curiosity and creativity in finding problem-related solutions. Contribute actively to a respectful, inclusive and open work atmosphere
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four years are expected to acquire basic pedagogical competency in the course of their fellowship period within the duty component of 25 %. Place of work is Department of Chemistry at Blindern/Gaustad
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within neuroscience, psychology, medicine, machine learning or biology or equivalent. Doctoral dissertation must be submitted for evaluation by the closing date. Appointment is dependent on the public
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Active participation in LALP Lab activities Required selection criteria You must have completed a doctoral degree in cognitive science or computer design/programming Training and experience with at least
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validation intimately connected to experimental validation. In this project, you will develop machine learning methods and apply them in an interdisciplinary environment spanning physics, neuroscience and
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approved. Desired qualifications and competencies PhD thesis relevant to the proposed research project Expertise in machine learning Additional expertise in one or more of the following: digital signal
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electrophysiological recordings in humans with behavioral experiments and advanced analytical approaches, including machine learning and statistical modeling. It has two main objectives: Develop a cognitive task for
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career Use and/or development of advanced stellar photometric/spectroscopic/spectropolarimetric methodologies Experience with machine learning techniques Experience with pipeline development and testing
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machine learning, distributed systems, and blockchain technologies. Your immediate leader will be the Head of the Cryptology Unit at the Department of Information Security and Communication Technology. The
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-principles electronic structure calculations Perform materials screening including machine learning to identify promising thermoelectric materials for cooling technology The successful candidate is expected